A Bayesian belief network (BBN) is a directed graph and an associated set of probability tables. The graph consists of nodes and arcs. The nodes represent variables, input data for which can be discrete or continuous; however the BBN must segment continuous data into parameterized ranges. The arcs represent causal or influential relationships between variables. More specifically, a BBN is a probabilistic graphical model that represents a set of random variables and their conditional independencies. It is a way of describing complex, probabilistic reasoning.
Machine learning is a field of computer science that uses intelligent algorithms to allow a computer to mimic the process of human learning. The machine learning algorithm allows the computer to learn information structure dynamically from the data that resides in the data warehouse. The machine learning algorithms automatically detect and promote significant relationships between variables, without the need for human interaction. This allows for the processing of vast amounts of complex data quickly and easily. This also allows for a computer to discover the structure of the BBN without specification by a human operator.
The machine learning models can be scored in different ways: Minimum Description Length (MDL), also known as the Bayesian Information Criterion (BIC), as well as Bayesian Scoring. MDL scoring provides a measure of the quality of a model. It trades off between goodness of fit and model complexity (parsimonious scoring). Goodness of fit is measured as the likelihood of the data given the model. Model complexity equals the amount of information required to store the model, subject to an inflator/deflator set by the user. The BBN networks and/or machine learning models have not been previously utilized in policy decision making processes of insurance plans or in selection of enrollees for disease management or care interventions.
Typically BBNs are used to predict the probability of known events. However, in some situations, there is a need to predict certain unknown events that are dissimilar to certain known events. For example, there is a need to detect new virus or malware that has not been detected before, to detect certain telecommunications network activities that are abnormal but unknown, or to detect aberrations in biological networks to identify potentially sick individuals before they become symptomatic. Conventional malware or network activities detection systems lack efficient ways for such purposes, while conventional clinical diagnostics evaluate individual system elements rather than a system as a whole.